MiniMax Image-01 text-to-image model generates high-quality images from text descriptions. Create diverse visuals across multiple styles and scenarios with natural language prompts. Supports multiple aspect ratios and custom dimensions. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Siap

$0.0035per run·~285 / $1
MiniMax Image-01 Text-to-Image is a powerful AI image generation model that creates high-quality images from text descriptions. Part of the MiniMax image-01 family, this model excels at understanding natural language prompts and generating diverse, creative visuals across multiple styles and scenarios. Perfect for content creators, designers, marketers, and developers building AI-powered applications.
Natural Language Understanding Simply describe what you want to see in plain text (up to 1500 characters), and the model generates corresponding images with impressive accuracy and creativity.
Flexible Image Dimensions Specify exact pixel dimensions from 512×512 to 2048×2048 pixels (must be divisible by 8) for precise control over output size. Common sizes include 1024×1024, 1280×720, 1152×864, and more.
Prompt Optimization Built-in prompt optimizer automatically enhances your text descriptions for better generation results, making it easier to achieve professional-quality outputs even with simple prompts.
Batch Generation Generate up to 9 images in a single request, perfect for exploring creative variations and selecting the best result for your needs.
Reproducible Results Use seed values to generate consistent results across multiple runs, essential for iterative refinement and maintaining consistency in production workflows.
Multiple Output Formats Receive generated images as direct URLs (24-hour expiration) or Base64-encoded data for immediate embedding in your applications.
Output Dimensions:
Output Formats:
prompt field (max 1500 characters)size parameter like "1024 * 1024" or "1280 * 720"num_images: Set 1-9 to generate multiple variations (default: 1)prompt_optimizer: Enable for automatic prompt enhancement (recommended for beginners)seed: Use a specific number for reproducible resultsPrompt Writing Best Practices:
Using Seeds for Consistency:
Batch Generation Strategy:
Generations return as:
Response includes:
Photorealistic: "A professional product photo of a luxury watch on a marble surface, studio lighting, shallow depth of field, commercial photography style"
Artistic: "An impressionist oil painting of a Parisian café in autumn, warm colors, loose brushstrokes, golden afternoon light"
Conceptual: "A futuristic cityscape at night with neon lights, flying vehicles, cyberpunk aesthetic, rain-slicked streets, dramatic perspective"
Character: "A friendly robot character with a round body, expressive LED eyes, metallic blue finish, standing in a modern laboratory, 3D render style"
Also available on WaveSpeedAI:
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/minimax/image-01/text-to-image with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Image 01 Text To Image below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "1024*1024",
"num_images": 1,
"prompt_optimizer": false
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/minimax/image-01/text-to-image" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d "$REQUEST_BODY")
TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
printf 'Submission response did not contain a prediction id
' >&2
exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
-H "Authorization: Bearer $WAVESPEED_API_KEY")
RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
case "$STATUS" in
completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
created|processing) sleep 2 ;;
*) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
esac
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/minimax/image-01/text-to-image";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');
async function requestJson(url, options = {}) {
const response = await fetch(url, options);
if (!response.ok) throw new Error(await response.text());
return response.json();
}
// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
method: "POST",
headers: {
"Authorization": `Bearer ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "1024*1024",
"num_images": 1,
"prompt_optimizer": false
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;
// 2. Poll until the prediction finishes.
while (true) {
const resultBody = await requestJson(resultUrl, {
headers: { "Authorization": `Bearer ${apiKey}` },
});
const result = resultBody.data ?? resultBody;
if (result.status === "completed") {
console.log(result.outputs);
break;
}
if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
await new Promise(resolve => setTimeout(resolve, 2000));
}import json
import os
import time
from urllib.request import Request, urlopen
api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "1024*1024",
"num_images": 1,
"prompt_optimizer": False
}
def request_json(url, data=None):
request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
with urlopen(request) as response:
return json.load(response)
# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/minimax/image-01/text-to-image", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"
# 2. Poll until the prediction finishes.
while True:
result_body = request_json(result_url)
result = result_body.get("data", result_body)
status = result.get("status")
if status == "completed":
print(result.get("outputs", []))
break
if status in {"failed", "cancelled", "timeout"}:
raise RuntimeError(result)
if status not in {"created", "processing"}:
raise RuntimeError(f"Unexpected status: {status}")
time.sleep(2)Image 01 Text To Image is a MiniMax model for image generation, exposed as a REST API on WaveSpeedAI. MiniMax Image-01 text-to-image model generates high-quality images from text descriptions. Create diverse visuals across multiple styles and scenarios with natural language prompts. Supports multiple aspect ratios and custom dimensions. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.
POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/minimax/minimax-image-01-text-to-image.
Image 01 Text To Image starts at $0.004 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.
Key inputs: `prompt`, `size`, `enable_base64_output`, `enable_sync_mode`, `num_images`, `prompt_optimizer`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/minimax/minimax-image-01-text-to-image.
Median end-to-end generation time on WaveSpeedAI is around 25 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (MiniMax). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.